期刊
JOURNAL OF IMAGING
卷 7, 期 2, 页码 -出版社
MDPI
DOI: 10.3390/jimaging7020034
关键词
prostate cancer; artificial intelligence; multiparametric magnetic resonance imaging; Gleason score; neoplasm recurrence; local
This paper reviews the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. A future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer is also provided.
Multiparametric prostate magnetic resonance imaging (mpMRI) is widely used as a triage test for men at a risk of prostate cancer. However, the traditional role of mpMRI was confined to prostate cancer staging. Radiomics is the quantitative extraction and analysis of minable data from medical images; it is emerging as a promising tool to detect and categorize prostate lesions. In this paper we review the role of radiomics applied to prostate mpMRI in detection and localization of prostate cancer, prediction of Gleason score and PI-RADS classification, prediction of extracapsular extension and of biochemical recurrence. We also provide a future perspective of artificial intelligence (machine learning and deep learning) applied to the field of prostate cancer.
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